Innovation in Electrical Power Engineering, Communication, and Computing Technology

Due to two significant benefits such as less voltage and high density of energy, the DC-to-DC converter has become very common today in the world’s electronic sector. The production of the DC-to-DC converter is therefore comparatively larger than that of normal AC-DC converters. The studies are developing multi-output DC-to-DC converters in latest years. These investigations focus on isolated as well as non-isolated DC-to-DC converters. A small input and output ripple sides, a minimum amount of switches, elevated voltage increase and lighter weight are the primary benefits of this type of converters. A single-input three-output SEPIC-Cuk-boost combination converter is analysed. It is working, and modelling and applications are also discussed. The solar panel used for the analysis is modelled by using its mathematical equations. Comparative studies of incremental conductance (IC) and Extended Perturb-Perturb (EPP) MPPT techniques are discussed for extracting highest power from the solar panel. The proposed solar-fed model is simulated by using MATLAB software. The experimental set-up of single-input to three-output SEPIC-Cuk-boost converter is developed, and the performance is compared with the simulated results. This proposed converter is able to generate two DC bipolar and one boosted DC output voltages.

The paper presents the comparative result analysis of calibrated JPEG images with and without cross-validation technique. Pixel-value differencing, LSB replacement, F5 and LSB Matching are used as steganographic algorithms. 25% of embedding is considered for the analysis. The images are calibrated before they are considered for analysis and relevant features are extracted. The classifier used is SVM with six various kernels and four types of sampling methods. The sampling methods are linear, shuffle, stratified and automatic. Radial, dot, Epanechnikov, multiquadratic, polynomial and ANOVA kernels are taken into consideration in this paper.

Smart grid is a bidirectional electric and communication network which includes various energy and operational devices including smart accessories, smart meters, nonconventional energy resources and energy-saving devices. Through two-way digital communication, it is used to supply electricity to consumers and domestic purposes. Smart grid technologies are enforced in India in order to reduce transmission and distribution losses. Demand side management is an important function of a smart grid and it helps to deduce the utilization costs of electricity. It allows energy suppliers to reduce peak load demand. The method which is proposed composed of modern system identification and it enables the user to schedule their loads. This potentially balances both supply side and demand side in order to make the system more efficient. However, due to lack of proper coordination among the agents, it may cause significant peaks in demand, which will in turn reduce efficiency of overall system. In order to correlate the agents in a decentralized manner, we are introducing a mechanism of decentralized demand side management (DDSM) in smart grid. Peak demand of domestic customers in the grid can be reduced through proper coordination among customers.

This is all about harmonic the current mitigation (as one of the major power quality issues) through the imaginary power owing to their precise and audacious action by shunt active filter (ShAF) build on sinusoidal current control strategy (SCCS) in a grid-tied PV system. The SCCS is employed to liberate sinusoidal current commenced from the supply additionally with a burly synchronizing (phase-locked loop) circuit, concise the controller for ShAF. Explicitly by a sinusoidal current control based controller, ShAF acts as a harmonic isolator amidst with load and supply. The performance of the said technique is evaluated using MATLAB R2016a under passive load (nonlinear load), and the observations are discussed. The efficacy of the ShAF in harmonics mitigation is validated through the proposed simulation. The total harmonic distortion (THD) of current and voltage ascertains controller’s practicability for the ShAF delivering harmonic separation of passive loads in the grid-tied hybrid energy system.

In this work, a novel bludgeon-shaped microstrip patch antenna with defected ground structure (DGS) is recommended for UltraWide Band (UWB) i.e., (3.1–10.6 GHz) applications with band notch characteristics. The encouraged antenna is simulated and fabricated using RT DURIOD 5880 substrate with epsilon value of 2.2 having the scale of 60 × 60 × 1.6 mm3. The circular-shaped patch is chosen with radius of 14 mm and 50 Ω microstrip line feeding technique is used for the simplicity of the design. The circular slots and circular-shaped patch are delivered to improve the radiation traits and overall performance of the recommended antenna. To acquire the UWB frequency of operation, rectangular defected ground structure (RDGS) is implemented in the ground plane. Further, three rectangular slots of different dimensions are etched to accomplish the band notch configuration. The recommended antenna is simulated using 3D electromagnetic simulation tool CST MICROWAVE STUDIO Version16. The evolved antenna is tested using MS46122B 20 GHz vector network analyzer. The effectiveness of the antenna is proven the usage of the specifications like return loss (S11), VSWR, and simulated radiation patterns.

At present, work on the 1200 kV transmission system is in progress in India. Research work is going on at National Test Station, Bina, where 1200 kV setup with single-circuit and double-circuit line is installed. In order to investigate the performance of ultra-high voltage (UHV) system, the design modifications and other technical studies related to the transmission system, its simulation study is required. A UHV autotransformer is the main part of any UHV transmission system. Early research suggested the use of single-phase three-winding transformer model of PSCAD software (4.2 version) to model an ultra-high voltage autotransformer. The novel method uses a bank of three ‘single-phase three-winding autotransformers’ and a three-phase star-star autotransformer with tertiary (with provision of a Star or delta connection of tertiary winding and choice of delta lag or lead) model of PSCAD 4.6 version. This paper presents the modeling and simulation and validation of 1200 kV UHV autotransformer using these models. The simulation results are validated and verified through the actual field test results, and it is found that the autotransformer module of PSCAD 4.6 software is suitable for simulation study of the UHV installation.

This paper deals with source current harmonics and voltage sag compensation of a solar photovoltaic (PV)-based distributed generation (DG) system using unified power quality conditioner (UPQC). Despite the several benefits of DG like excellent energy supply, reducing expansion of power distribution system, environmental friendly and so on, there are several challenges existing due to the integration of DG with the grid or operating it in stand-alone mode. Power quality (PQ) issue is one of the main technical challenges in DG power system. In order to provide improved PQ of energy supply, it is necessary to analyze the harmonics distortion of the system as well as the voltage sag and swell. The UPQC has been widely useful, and it is confirmed to be the best solution to diminish this PQ issue. This paper explores the detail of PQ impacts in a DG (comprising of solar PV) system operates in stand-alone mode. The voltage sag compensation with voltage and current harmonics is estimated under varying load situation with a control scheme like modified synchronous reference frame technique. The proposed model is developed in MATLAB/SIMULINKR, and the result obtained validates the superiority of proposed technique over others in terms of harmonics elimination and sag compensation.

Today cloud computing is the most rising innovation due to its ground-breaking and significant power. It is a type of Internet-based computing which enables to provide various services in cost-effective manner on user’s demand. Virtualization, grid technique and utility computing are the most popular emerged technologies, which are used in cloud computing and make it most powerful. But still cloud computing has number of critical issues, such as security, load adjusting, adaptation to non-critical failure and so forth. The gigantic development of cloud computing will cause the overburden on the server. In this way, it will lead to degrade the performance of networks. A decent load balancing adjusting can make cloud computing increasingly productive and improves execution of client fulfilment. This paper exhibits the far-reaching survey of cloud computing and load balancing techniques.

In this article, a novel heuristic moth flame optimization (MFO) technique has been suggested to optimize the gains of fractional-order proportional–integral–derivative (FOPID) controller and PID controller over a hybrid source power plant to analyze the load frequency control (LFC). Each area comprises dish-stirling solar thermal system (DSTS), conventional steam power plant (SPP), and a geothermal power plant (GTPP) to scrutinize the vigorous performance of power system for load frequency control. For more analysis, governor dead-band (GDB) and generation rate constraint (GRC) of the SPP have been considered as 0.036 and 0.003, respectively. The dynamic responses of two equal area systems are observed by implementing FOPID and PID controllers independently. The investigation exposes the dominance of FOPID controller over PID controller.

In the literature, there are hundreds of population-based optimization algorithms, which are inspired by nature. These algorithms have been proposed to solve different problems of different research area. Among them, teaching-learning-based optimization (TLBO) algorithm is one, which is based on learner’s behavior in the classroom. For improvement of performance of this algorithm, number of variants of TLBO algorithms already have been proposed. This paper compares the performance of these variants of TLBO in terms of clustering of unlabeled data sets. Here, it has been shown how different variants of TLBO can be used to find the clusters of a user-specified cluster numbers. These algorithms are evaluated by using some real-life datasets and compared their performances by some statistical tests.

Aerobic rice cultivation is nowadays gaining importance due to the constraints in the availability of required amount of water for traditional rice growing system. An attempt has therefore been made to develop a portable solar photovoltaic-powered (off-grid) drip irrigation system for aerobic rice cultivation, which is a water-saving and less-water-consuming rice production system without any compromise with decline in yield. It is suitable mostly in the water-deficient, non-irrigated, and off-grid areas. There may be the saving of 40–45% of water for irrigation purpose compared to the conventional method, mitigation of 0.55 million tons of CO2 with the replacement of existing diesel and electric pump sets and 0.2 million tons of CH4 from 4.0 million hectares of rice fields in the state of Odisha, India, through the system, developed by adopting aerobic rice cultivation. The pay- back period of the setup is estimated to be 4 years and total annual saving of Rs. 675 crores due to reduction in the use of electrical energy and petroleum fuels through the existing pump sets in Odisha. Monthly income of Rs. 4000/– throughout the year was achieved by adopting aerobic rice cultivation in 1 acre (0.4 ha) of land.

The traditional Internet has been subjected to revolutionary change by the concept of Internet of Things (IoT) and has added new dimensions in the world of human-centric services. This concept basically indicates the ability of different devices to connect with each other and communicate effectively. But this revolution comes at the cost of increased demand in the number of sensor nodes that needs to be assimilated in a network. This problem is also followed by the presence of an effective network solution that has the ability to contain these requirements consequently. The wireless sensor network generally includes energy-limited devices, so energy-saving technologies are of growing concern. Latency, range coverage and bandwidth are among other issues in this IoT. LoRa emerged as the emerging solution to the above problems which has the main intention to save energy. This provides long-range, low-power data transmission rate and provides higher efficiency in wireless data communication. LoRa turns out to be the potential device to realize a large number of Internet of Things applications. A large number of papers have reported the performance and efficiency of LoRa-based wireless area network (LoRaWAN) in the fields of outdoor and radio communication, but the maximum of its potential is still unexplored. This paper effectively tries to put down some of the applications of LoRa in the field of predictive maintenance. The detailed hardware specifications and the experimental results are furnished to establish the supremacy of LoRa over other network devices in predictive maintenance.

This paper presents a project based on Proportional–Integral–Derivative (PID) for the implementation of load controller for three-phase synchronous generator. Electronic load controller is a device which is related to power electronics method of controlling, managing and monitoring frequency of a system. Mostly, in rural areas, we do not have access to grid for power. There pico- and micro-hydropower can be implemented to provide power. For persistent operation and control of a three-phase synchronous generator, an electronic load controller has been implemented rather than using speed controller governor which is much more expensive. With the help of the proposed device, load output can be controlled, and thus, frequency can be maintained constant which again reduces worst case of overloading on generator. Therefore, with the help of the proposed scheme, protection of both generator and user’s load can be maintained. The proposed system can play a vital role in run-off-river type hydropower station because there is no point of saving water. Thus, the proposed system can be used to minimize the overall cost of installation of the hydropower plant up to a large extent in rural areas where we do not have access to grid connection for electricity.

Social media usage has seen a dramatic rise in the recent years. With the use of just 140 characters on Twitter, people can voice their opinion on any subject. Various techniques have come up to identify the sentiment of these tweets so as to reach appropriate conclusions. Sentiment analysis of tweets related to politics is theorized to be able to identify public sentiment toward candidates and predict election results. The methodologies have been broadly classified into lexicon-based approaches and machine learning-based approaches. Algorithms like Naive Bayes (NB), support vector machine (SVM) and neural networks are used in machine learning-based approaches. Owing to their greater flexibility, they are more useful than lexicon-based approaches when it comes to political tweet analysis. A survey of all these approaches reveals that SVM provides accuracy over 70%, making it the most efficient algorithm for political sentiment analysis of tweets.

This study reveals the load frequency controlLoad frequency control (LFC) of an unequal four-area thermal system with HVDC link considering suitable generation rate constraint. Performances of controllers such as proportional–integral–derivative (PID) and fractional-order PID (FOPID) are separately evaluated in the system. At first, plug-in electric vehicle (PEV)Plug-in electric vehicle (PEV) is applied to the thermal unit system to provide the stability for fluctuated load demand, which is widely expected from customer side as a spinning reserve. For the better quality of solution and improvement of convergence property, a hybrid differential evolution particle swarm optimization (DEPSO) technique is used here. DC tie-line is introduced here which improves the stability of system as compared to AC link. Comparison of dynamic responses corresponding to above controllers reveals that FOPID outperforms better than conventional PID controller. Also, a comparison has performed with and without PEV applied to four-area systems. The simulation is carried out by using MATLAB/SIMULINK software with step load perturbation (SLP).

The restructuring of electrical power industries creates competitiveness among the market players, due to which the complexity of load frequency issues is gradually increasing in nature. So for mitigating these load frequency issues controllers are used. This chapter proposes automatic generation control (AGC) for two interconnected control areas, each consisting of two power generation sources, i.e. reheat steam turbine in conjunction with nonlinear generation rate constraint and gas turbine generation in a restructured market environment. Sunflower optimization (SFO) algorithm is used for optimal tuning of proportional, integral and derivative (PID) controller considering the integral square error as the objective function. For analysing the market dynamics, the concept of area participation matrix (APM) and DISCO participation matrix (DPM) has been simulated. The effectiveness of this two-area system has been tested with various market scenarios like poolco trading, bilateral trading and contract violation. The yield of the proposed algorithm shows better performance in contrast to the other methods used for tuning the PID controller.

Nowadays, there is increased interest toward the use of renewable energy resources for electricity generation in developing nations. The properties like increased reliability, good power quality and eco-friendly operation of the resources force the mankind for greater acceptance. The solar photovoltaic cell of silicon material is the main source of power generation due to social, economical and environmental benefits with public support and government incentives. Both the analytical and simulation methodologies are used for evaluation process of reliability. The randomness of PV can be covered by the implementation of Monte Carlo simulation methodology. The reliability of the distribution system can be evaluated by using time sequential Monte Carlo simulation method. At each load point of IEEE 33-bus system, the failure rate and repair time are calculated. Reliability indices like SAIFI, SAIDI and CAIDI are evaluated at constant output of rooftop PVs at each load point.

Scientific study revealed that eyes are the best indicators of many diseases like glaucoma, diabetic retinopathy, hypertension, and stroke. By examining the segmented retinal blood vessel network, ophthalmologist can get the information regarding the abnormality. The objective of this research is to provide reliable segmented retinal blood vessel to assist the ophthalmologists to figure out the abnormality precisely. In this work, enhanced Gabor filter in multiple orientation is employed for the retinal blood vessel extraction. The suggested method combines sharpening operation and median filtering with Gabor transform to get enhanced Gabor transformed images. Finally, to get the segmented output hysteresis thresholding is applied on the enhanced Gabor transformed images. As hysteresis thresholding takes into consideration the connectedness between neighboring pixels, it performs better in segmenting the vessels. The suggested integrated approach has improved the accuracy and specificity. Experiment on 20 retinal images of DRIVE database indicated 95.06% accuracy and 98.83% specificity.

In recent years, many industrial applications need higher-powered appliances. It is difficult for a single semiconductor switch to connect directly for medium-voltage appliances. In high-power and medium-voltage circumstances, a multi-level power inverter design was provided as an alternative. A multi-level inverter is flexible to use in renewable source, and high-power rating is achieved. Multi-level inverters are categorized into two parts, (1) multi-level symmetrical inverters and (2) multi-level asymmetrical inverters. Here, the asymmetric multi-level inverter is called a dual-DC-port asymmetric multi-level inverter (DP-AMI) that uses only one topology to interface a low/high-voltage DC port and an AC port at the same time. DC source having low voltage like the battery or photovoltaic (PV) modules can provide power directly to the load within a single power conversion stage. It can improve the overall conversion efficiency of the system. The derivation scheme for DP-AMIs topology is given. The principle of operation, schemes used for modulation, and features of DP-AMIs are studied. This inverter’s working principle was evaluated, and the expression of output voltage was obtained. Furthermore, the inverter is integrated with the photovoltaic cell, and its maximum power point is tracked. Finally, the outcome of the DP-AMI is compared to the cascaded H-bridge multi-level inverter. After the simulation, it has been found satisfactory results.

Fuzzy control as a method used in control design has been predominant when the plant mathematical model is unavailable. Granular computing has been seen as a process which speeds up the fuzzy methodology when combined to the fuzzy control design. Inverted pendulum control and stabilization are a classical problem investigated in this work. The stabilization of the pendulum is achieved with an advanced fuzzy granular computing-based controller under the influence of time delay. The controller which was a combination of fuzzy type-2 controller along with classical PID controller achieved the control. However, it was seen that the impact of time delay with advanced fuzzy controllers is significant even if there was a unit transportation delay in the system the output response suffers which becomes a major drawback and which indicates the need for possible boundaries in time delay systems.

Photovoltaic (PV) modules are installed in the field to deliver possible maximum power to the load. However, the PV modules encounter various electrical faults during operation that results in severe power loss, failure and performance degradation of the system. Also, the lack of fault knowledge among the PV manufacturer and installers has resulted in severe accidents and fire hazards in some cases. The scope of this paper covers the description on various electrical faults, their occurrence and impact on the performance of PV modules. The faults include interconnect and connection faults, bridge and earth faults, shunt path development faults and mismatch faults that commonly occurs in the field-installed PV systems. The effect of these electrical faults on the equivalent circuit and characteristics curves of the PV modules has been studied using MATLAB/Simulink. The analysis presented clearly indicates the adverse effect of these faults in the power generation and performance of the PV modules. Also, a short survey on various fault detection techniques proposed in the literatures has been done.

This manuscript presents a method based on S-transform (ST) and extreme learning machine (ELM) to identify the causes of voltage sag. Exact recognition of voltage sag causes (VSCs) can help decrease the problems initiated due to voltage dip in electric power system. ST is a well-known time–frequency analysis technique. Initially, the extracted voltage signals are pre-processed through ST and several statistical features are extracted, which are later applied as inputs to ELM classifier for voltage sag cause detection. Here, three significant causes are simulated for voltage sag in MATLAB/Simulink, like (i) single-phase and three-phase fault, (ii) starting of induction motor and (iii) energization of transformer. The performance of the proposed technique is compared with other existing voltage sag cause classification techniques. The classification results indicate the ability of the proposed technique for detection and classification of VSCs more accurately.

Advent of deep learning in 2012 has revolutionized the plethora of researches including bioinformatics. This revolution is complemented by the progress in hardware technology and big omics data. Software mimicking human brain, powerful hardware and data to harness, these trio factors have benefited researches in image recognition, speech recognition, natural language processing and more so the life science domains involve omics. Deep learning has resolved the problem of transforming data into knowledge with much acceptable accuracy compared to their traditional counterparts. This paper condenses the problems addressed by different deep learning architectures in proteomics, which is a notable field of life science. The paper also summarizes the use of accelerators in addressing the problems.

In this paper, an algorithm depending on limit cycle oscillator-based frequency-locked loop is used to extract fundamental component of load current. The extracted fundamental components are used to control distribution static compensator (DSTATCOM). The major function of DSTATCOM is to make source current sinusoidal for compensation of power quality problems. This reduces the reactive burden on the source and helps in making source current sinusoidal. The reference grid current has been produced by extracting fundamental components from each phase of load current using limit cycle oscillator (LCO)-based frequency-locked loop (FLL). An optimization algorithm named multi-verse optimization (MVO) which is based on population has been used for estimation of PI gains as error controller in various modes of operation of DSTATCOM as mentioned. The error minimization of DC bus voltage and terminal voltage has been formulated as an unconstrained optimization problem, and gains have been estimated. To maximize the benefits of the MVO optimization process, the local search accuracy has been improved. This improvement has been achieved by increasing the wormholes in the universe. The suggested control scheme with gain values from the MVO optimization technique has controlled issues like reactive power compensation and mitigation of grid current harmonics caused due to an identified harmonic producing load, i.e. a three-phase AC voltage controller with resistive loads. This work provides results of d-SPACE-based realization of the suggested limit cycle oscillator-based frequency-locked loop algorithm for DSTATCOM.

Due to uncertainty in solar radiation caused by cloud cover and other environmental conditions, it is necessary to accurately predict solar irradiance for time ahead planning and scheduling. In this paper, a comparative analysis of 1 h ahead forecasting of global horizontal irradiance using various popular machine learning technique and deep neural network is determined. The forecasting accuracy is analyzed using various error metrics such as mean absolute error (MAE)and mean square error (MSE). The number of time lags influencing 1 h ahead forecasting is determined using autocorrelation and partial autocorrelation function. The hyper-parameters of various machine learning techniques are tuned using random search method. It is observed the deep neural network provides the least prediction error as compared to other machine learning techniques.

The autonomous underwater vehicles (AUVs) are the independent marine robots employed in many undersea operations starting from the field of underwater research to commercial oil industry surveys. The path planning of a team of AUVs is necessary as it is required to find a safe route from the start to endpoints for the team of AUVS to accomplish any mission goal. The group of AUVs has to trace a time or energy optimal locus in order to reduce the cost of any operation. This paper proposed a hybrid grey wolf optimization (h-GWO) algorithm for path planning of multiple AUVs by integrating features of genetic algorithm (GA) and grey wolf optimization (GWO). This allows a team of AUVs to move in an obstacle rich environment without colliding with obstacles and with each other. Here, path cost is a summation of distance travelled and penalty of collision. Simulation results are obtained for three AUVs in a three-dimensional grid map with static obstacles by applying GA, GWO, and h-GWO algorithm. A comparative study of obtained results made on the basis of computational time, path length and path cost. The results of the hybrid algorithm overcome the disadvantages of GA and GWO and provided a cost-optimized path for operations that are not time-sensitive.

In this paper, a discrete wavelet transform and a radial basis function (RBF) kernel-based support vector machine are combined to form a hybrid technique for a short distance radial distribution line. The main objectives of the proposed method are to classifying the shunt faults and estimating its locations. Unlike a decision tree-based hybrid method, this technique is easy to implement and computationally less burden. Here, three-phase line current samples have been utilized to generate the features. Importantly, feature extraction has been handled by Daubechies mother wavelet. Six features (i.e., mean, standard deviation, energy, entropy, skewness, and kurtosis) of line currents have taken into consideration. The data extracted from the feature generation stage contain redundant feature values. Therefore, the principal component analysis tool has been applied to reduce the redundancy in the dataset by optimizing the features. Finally, the optimized features are sent as inputs to the kernel-based SVM for fault classification and estimating the location. MATLAB/Simulink simulation environment and Python programming interface have been utilized to test the performance of the developed model.

The power output of a photovoltaic (PV) module depends on the geographical locations (latitude, longitude and solar irradiance), tilt angle and its climatic condition (humidity, temperature) and shading due to soiling, bird droppings, etc. This paper deals with the power loss in solar PV system due to deposit of dust particles on the PV surface. The loss of power due to soiling was studied for a rooftop solar PV system installed in SOA (Deemed to be University), Bhubaneswar, India. It was observed that power loss to an extent of 3.09% per day occurred during non-monsoon months, while it reduced to 0.93% per day during monsoon months due to accumulation of soil on PV modules.

Ant colony optimization (ACO) is a modern metaheuristic advance for resolving firm optimization problems. ACO is based on perception of swarm intelligence (SI). Many algorithms that are motivated by scavenging activity of ant colonies were utilized to result of complex distinct optimization problems. This paper intends to provide a detailed idea about the importance of ACO for data mining problems with a broad analysis on the structure of ACO as well as its applicability from 2015 to 2020. Many variations of ACO have also been utilized for solving diverse complex problems of data mining area. The main intent of this study is to inspire the researchers of optimization board to make use of ACO for explaining to those kinds of problems that were not used yet.

This manuscript presents a SIMULINK model of inverted pendulum (IP) and design of a linear quadratic regulator (LQR) to control of cart position (CP) and angular position (AP) of the pendulum under uncertainties and disturbances. For designing of the LQR, a fourth-order state-space model of the IP is taken. The LQR is a novel approach whose gains dynamically vary with respect to the error signal. The validation of the improved control performance of LQR is established by comparative result investigation with other published control algorithms. The comparative results clearly reveal the better response of the proposed approach to control the system dynamics within the stable range with respect to accuracy, robustness, and ability to handle uncertainties.

Indoor mobile platform localization plays an important role in autonomous navigation. This paper describes the development of a mobile platform localization approach based on wheel odometry and inertial measurement unit (IMU) sensors throughout the travel path. The correctness of the estimated position determines the accuracy with which the trajectory can be predicted. A number of experiments were carried out to understand the variation in IMU data with the sensor placed at different distances along the axis of the robot and its effect on heading angle of the mobile platform and the resulting trajectory. In the east and west directions, as distance between IMU and midpoint of axis joining left and right dc motors becomes greater than 8 cm, effect of positioning of dc motor starts reducing on heading angle θ and goes down to zero at distance greater than 12 cm. In north and south directions, as distance between IMU and midpoint of axis joining left and right dc motors becomes greater than 2 cm, effect of positioning of dc motor completely vanishes. The paper details the experiments carried out, results obtained, and discusses measures to improve the localization accuracy.

Handwritten digit recognition plays an important role not only in computer vision but also in pattern recognition. Handwritten digit recognition is the competence of a machine to receive, calculate and decipher a human handwritten input from sources such as handwritten manuscripts, especially created before the advent of a digital revolution and digital images.This work implements the system to read the handwritten digits with a custom novel method identical to the amalgamation of different techniques, including principal component analysis, support vector machine and K-nearest neighbours to recognize and classify handwritten digits into their respective labels. PCA algorithm finds out the best linear combinations of the original features so that the variance along the new feature is maximum. Recognition of characters is done using KNN nonparametric machine learning algorithm, and SVM lowers the generalization error of the overall classifier. The proposed work does the analysis on digit data set having a total of 70,000 image samples. The performance of the system is analysed using different measurement metrics like precision, recall, f1 score and support, and the recognition of the patterns in the images shows the result with classification accuracy of 97%.

The research work proposed in this article involves a nature-inspired technique of salp swarm algorithm (SSA) which is proposed for finding the optimal reactive power dispatch of an interconnected power network. Oppositional-based learning is a promising technique for improving convergence and is used in this work coupled with SSA algorithm as a new hybrid method of oppositional salp swarm algorithm (OSSA). The proposed techniques are successfully implemented on standard IEEE 57 benchmark system for deprecating of real power losses as well as overall cost of operation of the power system along with retention of bus voltages under acceptable limits. The performance of the proposed algorithms is also examined with inclusion of optimal thyristor-controlled series compensator (TCSC) placement and sizing. The identification of weakest branch in the power network is initially done for optimal TCSC placement and it is tendered through line stability index method (LSI).

The grid-connected photovoltaic (PV) system is gaining more and more attention due to its associated abilities of uninterruptable electrical power supply. The increasing demand for sustainable energy production has led us to focus on the power electronic circuit interfaced with the renewable energy sources. To increase the efficiency of the produced solar energy, DC–DC boost converters with an advantage of maximum power point tracking (MPPT) are considered as a far-fetched solution. In this paper, a PV module with MPPT has been designed. This study also includes the design and control of inverter as well as cascaded inverter with SPWM, which helps reduce the total harmonic distortion (THD) and gives a better dynamic response and accuracy in tracking references. A comparison between different inverter topologies has been proposed here for better result analysis.

As India is slowly transiting from the fossil fuel to the renewable energy format for power generation, the concept of energy security, efficiency, usage of clean energy sources and less environmental degradation are the points of focus. The high capital cost involved in the setting up of a solar PV system makes it necessary to evaluate the feasibility based on the economic indices. The work proposed in this article is to evaluate the payback periods for a solar ground-mounted photovoltaic (PV) system installed in the Tirupati Airport (Airports Authority of India) and reflect on the economic feasibility for the same. The results obtained indicate that the investment is feasible, both technologically and economically. The payback periods and the carbon emissions are reduced drastically and making it a clean energy technology.

This paper presents a comparative analysis between the two controllers, PI and fuzzy logic controllers that used for power factor correction using DC-to-DC single-ended primary-inductor converter (SEPIC) converter. MATLAB/Simulink models of SEPIC converter are developed to improve the power factor and output voltage regulation. Average current control technique is used for input power factor correction to get a regulated voltage at the output PI or fuzzy logic controller (FLC) is adopted. The system is tested at both steady-state and transient conditions, and its performance is then estimated and compared in terms of various parameters like total harmonic distortion (THD), input power factor, output voltage ripple for PI and FLC.

Microgrid and smartgrids are quickly moving from laboratories/demonstration benches to being deployed in increasing number across wide range of applications along with integration of renewable energy sources. This paper attempts to (i) Explain the concept of renewable energy-based microgrid/smartgrids and their relevance in solving India’s energy needs in a smart and sustainable way. (ii) Describes the various initiatives taken by Govt. to achieve the smartgrid vision of India along with brief on acts/policies enabling Renewable Energy Integration. (iii) Tracks the present status on smartgrid/microgrid activities across various parts of the country and does a comparative study on features of those projects. (iv) Analyzes the key benefits, opportunities as well the challenges faced during implementation of such smart and sustainable projects. (iv) Lastly, the author proposes a brief framework for deployment of new projects concerning microgrids/smartgrids.

This chapter contains the non-integer proportional–integral controller tuned by firefly algorithm for the regulation of frequency. Here, the results of the two-area system using PI controller are challenged using the new non-integer PI controller which expectedly gave the better results as compared to the standard PI controller. The system which is considered here for the study is the two-area interconnected system: One area has the thermal system, and the other one contains the photovoltaic system. The firefly algorithm helps here to find the controller’s scaling factors which help the system to come back to the normal state after the system is subjected to the 1% step load perturbation. The results of the surveyed systems with different controllers are compared on the basis of peak overshoots, undershoots, and settling time.

With the extensive demand for energy harvesting systems from various renewable resources, researches have been carried out in several areas among which thermo-electric generator (TEG) based system is an emerging one. In this paper, eight solar PV modules are interconnected in numerous fashions in order to investigate the behavior of the hybrid system. The transiency of the solar PV modules inside the network has been examined under Healthy Irradiance and Sectional Irradiance. Rise in solar concentration tends to decrease the solar PV module efficiency and this curse to solar PV becomes the boon to the TEG giving rise to higher power output at the terminals. Incorporation of solar photovoltaic (SPV) and thermoelectric (TE), termed as solar photovoltaic-thermoelectric (SPV-TE) hybrid system is found to be a very promising technique to broadening the utilization of solar spectrum and enhancing the power output effectively-cum-efficiently. This hybrid architecture caters electrical energy with additional thermal energy that signifies upon harnessing of solar insolation in an exceptional way.

Some of the issues in linear drive are critical combination of an air gap, inadequate use of magnetic flux, external vibrations with disturbances, low dissipation of thermal energy, etc. With linear drives, these problems have become drawbacks to extend them to any application. Multipole Field Electromagnetic Launching System (MFELS) is a system in the linear electromagnetic drive family that can provide enough linear force with minimal problems. Core type MFELS is considered for this paper and an attempt is made to implement Core type MFELS for the application of the launching system for aircraft catapult. A step-by-step iterative type design algorithm is developed. Exit velocity is considered to be the objective feature and the accelerating coil’s size, diameter and thickness are considered to be constraints. Comparison results for various pole cases were tabulated.

In this paper, analysis of fractional order passive RC high-pass filter circuit is presented. The time-domain expressions for different values of fractional order α were calculated. The output of fractional-order high-pass filter for order α has been simulated by MATLAB software. The effect of fractional order α on frequency response is observed. The design of these filters using an approximation of the fractional Laplacian operator is outlined. A fractance device of order α which uses usual expression sα is analyzed and presented using continuous fraction expansion (CFE) method. The fractional-order operator α is rationalized approximately by using different methods (Oustaloup, Newton and CFE method) that are presented and compared with the ideal response of circuit. The fractional-order circuits have better design flexibility than the integer-order circuits. The performance of integer-order circuit is improved by the replacement of fractional component as it has greater degree of freedom.

In this article, a spider monkey optimization technique (SMO) is suggested to make the gains of the controller in harmony with the hybrid plants to analyze the load frequency control (LFC) using two degree of freedom (2DOF_PID) and the degree of freedom (3DOF_PID). Each area comprises solar thermal system (DSTS), conventional steam power plant (SPP) and a geothermal power plant (GTPP) to scrutinize the vigorous performance of the system for load frequency control. For more analysis, governor dead-band (GDB) and generation rate constraints (GRC) of the SPP have been considered 0.036 and 0.003, respectively. The dynamic responses of the systems are observed by implementing different controllers independently. The investigation exposes the dominance of 3DOF_PID controller over other controllers.

In this paper, the classification of normal controls (NC), very mild cognitive impairment and mild cognitive impairment (MCI) from structural magnetic resonance imaging (MRI) are proposed, based on the discrete wavelet transform (DWT) and reduced deep convolutional neural network (RDCNN). Multi-resolution analysis using DWT is applied to the digital images for decomposition purposes. The automatic feature extraction, selection and optimization are performed using the proposed RDCNN. The classification accuracy and learning speed of the DWT-RDCNN method are compared with RDCNN by taking the MRI data as input. The superior classification accuracy of the proposed DWT-RDCNN method over RDCNN method as well as other recently introduced prevalent methods is the major advantage for analyzing the biomedical images in the field of health care.

The inflation of clean, efficient, sustainable, effective, secure, and reliable electricity demand has been triggered much interest for distribution generation at a miraculous and quickened pace. The necessity of reliability enhancement, diversity of fuel, cutback of greenhouse gases, severe weather fluctuation, etc. has stimulated the inclusion of Microgrid concept not only in utility level but also in customer and community level. Incorporation of solar photovoltaic (SPV) and thermoelectric (TE), termed as Solar photovoltaic-thermoelectric (SPV-TE) hybrid system is found to be a very promising technique to broadening the utilization of solar spectrum and enhancing the power output effectively-cum-efficiently. This hybrid architecture caters electrical energy with additional thermal energy that signifies upon harnessing of solar insolation in an exceptional way. This paper portrays on implementation of the aforementioned SPV-TEG coupled system in Non-conventional Distribution Generation in order to retrieve enough power from SPV array giving rise to higher active power delivery to the system and lower the reactive power absorbance by the system. The comparative analysis is done under subsystem such as SPV-TEG-Wind Energy system (WES) over SPV-WES on the basis of active and reactive power. The system is modeled, analyzed and validated under MATLAB-Simulink environment.

This manuscript presents a SIMULINK model of Glucose Metabolism (GM) process and design of a Fuzzy Logic Controller (FLC) to regulate the Blood Glucose (BG) concentration in Type-I Diabetes Mellitus (TIDM) patients. The FLC is a novel approach whose gains dynamically vary with respect to the error and change in error signal. The validation of improved control action of FLC is established by comparative result investigation with other published control algorithms. The comparative results clearly reveal the better performance of the proposed approach to control the BG concentration (level) within the normoglycaemic range in terms of accuracy, stability, and robustness.

In the power sharing scenario, maintaining power quality at its standard value is a challenging task. Insertion of various types of nonlinear load in the LT line not only deteriorate the quality of power flow but also unwanted harmonics are injected to the system. To maintain stability in the utility system adhering to quality of power flow, different least mean square (LMS) based adaptive control schemes are adopted. Comparative analysis between those LMS-based control algorithms is cited in this paper following steady and dynamic state analysis. In this paper, comparative dynamic analysis between LMS, sign error-LMS (SELMS), sign-data LMS (SDLMS) and sign-sign LMS (SSLMS) is made to evaluate the performance of the said algorithms.

This paper presents a comparative analysis between different adaptive filters such as LMS, LMF and RLS to study the dynamic performance of PV-DSTATCOM system. Various power quality issues such as power factor correction, reactive power control and harmonic elimination are addressed. To nullify the power quality issues and to adhere to the grid codes DSTATCOM is placed in shunt at the point of common coupling (PCC). In PV-DSTATCOM mode it maintains power flow to grid as well as to load under unity power factor and in DSTATCOM mode it sustains the load demand by maintaining power flow from grid to load. In addition, extraction of in phase and quadrature components of polluted load current is presented so as to produce suitable gate pulse for VSI. Performance evaluation of these filters is done on the basis of comparison of estimated active weight during unbalanced loading condition.

The aim of this paper is to design a fractional order proportional-integral-derivative controller (FOPIDC) for a vehicle suspension (VS) system to improve the ride comfort by absorbing the shocks due to a rough and uneven road. In this control strategy, the conventional proportional and integral controller (CPIC) is re-formulated with fractional orders of the integrator and differentiator to improve the control performance. The FOPIDC is a novel approach whose gains dynamically vary with respect to the error signal. The validation of the improved control performance of FOPIDC is established by comparative result investigation with other published control algorithms. The comparative results clearly reveal the better response of the suggested approach to control the oscillation of the VS system within a stable range with respect to the accuracy, robustness, and capability to control uncertainties.

This paper presents mutation volatilization-based water cycle algorithm (MVWCA) to improve the performance of a photovoltaic (PV)-based microgrid system by optimizing different regulators gain parameters during grid normal and uncertain situation. An inclusive optimization of different regulators gain parameters is studied by minimizing the cost function which constitutes active and reactive power losses at the VSC. A weak microgrid is considered to realize the prominent impact of different less inertia contributing element-based system. An auxiliary battery storage (BS) is integrated with PV to compensate the load during insufficient PV generation. The PV–BS generation is incorporated into the microgrid through a voltage source converter (VSC) using a second-order phase-locked loop (PLL) system to achieve grid synchronization. The proposed microgrid is modeled in the MATLAB script platform. Different rigorous case studies are realized to evaluate the performance of the proposed technique.

In this paper, a new modified water cycle (WC)-based optimization is incorporated for independent distributed generation controllers’ (IDGCs’) gain parameters tuning. Photovoltaic (PV)-based distributed generation (DG) is considered for active distribution network/microgrid tied operation, where the IDGC feedback path is designed with fuzzy Proportional-Integral (PI) scheme. The PI controller is linear and hence unable to address operational uncertainties (e.g. variation in irradiation profile, partial shading, symmetrical/asymmetrical faults, load variations, etc.) for a PV-based DG incorporation. The fuzzy rule-based adaptive gains are also limited towards unbounded uncertainties (grid connected to autonomous mode operation). Thus, to improve the system stability with effective feedback gain parameters’ selection, the modified WC algorithm is designed in this paper. The proposed WC scheme is included with a sinusoidal chaos map to address the nonlinearities. The performance of the proposed IDGC feedback path is evidenced under various grid operational contingencies. The efficacy measure of proposed scheme is recorded in terms of dynamic oscillation damping. This performance validation is implemented in MATLAB Script environment.

In this modernizing and developing world, consumers are demanding fresh, high-quality power. However, in distant locations or mountainous regions, it is not feasible for the utility grid to offer clean energy as it involves lengthy transmission, power shading and failures which is a primary reason for a large voltage drop. In an attempt to mitigate all these problems, isolated framework is introduced in this paper, comprising of a single-phase self-excited induction generator, an electronic load controller and a capacitor bank, which have been used for its self-excitation function. Self-excited induction generators operate in the saturation region. This creates poor voltage regulation at the load terminal, with the aim of alleviating this problem, and the electronic load controller is connected in parallel across the load. The electronic load controller comprises an unrestrained bridge rectifier, a chopper circuit and a dump resistive load. It works in a smart manner, i.e., the complete power at the end of the customer and the dump load is always retained at a steady value, and to resolve this issue, a control protocol is disclosed in this paper.

Some of the devices that are used in industrial, commercial and residential applications need DC supply for their proper functioning and operation. The devices that are used for conversion of AC supply to DC are basically behaved as non-linear loads and thus have non-linear input characteristics, which results in production of non-sinusoidal line current. Also, current comprising of frequency components at multiples of line frequency is observed which lead to line harmonics. Due to the increasing demand of these devices, the line current harmonics pose a major problem by degrading the power factor of the system thus affecting the performance of the devices. Hence, there is a need to reduce the line current harmonics, so as to improve the input power factor (IPF) of the system. This has led to designing of IPF correction circuits using new buck–boost converter topology. In this article, the authors used this circuit particularly for electric vehicle (EV) battery charging from AC supply. A passive power factor correction circuit with new buck–boost converter (BBC) is implemented for improving the IPF. Hysteresis current control (HCC) technique and PID controllers are used for both voltage regulation and IPF correction.

Rivers are the major freshwater sources. Good health and well-being require clean and good quality water. Recent time has witnessed severe degradation of water quality owing to heavy industrialization, agriculture, and anthropogenic activities. Such high levels of pollution can be health threatening. Hence water quality monitoring plays a vital role in assessing the water quality conditions of water resources; report the spatial and temporal variations in the conditions, identifying the cause and location of water pollution. This research work focuses on developing wireless sensor nodes capable of real-time analyses of the various physicochemical characteristics of any water resource. The work may offer a small leap towards providing safe water by updating the authorities through a web-based portal and mobile phone platforms regarding the contamination status so that preventive measures can be devised in time.

The conventional PI controller is most commonly used in vector control of ac motor drive system. However, this conventional control technique is found to be incapable of achieving the necessary regulation, dynamic response, and stability requirement which makes it unsuitable for nonlinear load, any kind of parametric variation and external disturbances. This paper presents sliding mode control (SMC) technique for controlling the speed of PMSM drive which provides high tracking performances to PMSM drive in steady-state as well as transient condition. Both the conventional PI controller and sliding mode controller have been analyzed for IPMSM drive system in the steady-state and transient state with constant and step load and speed variation. To investigate the dynamic performance of motor using both the strategy of speed controller, i.e., PI and sliding mode controller are designed and tested by the help of MATLAB-Simulink environment.

It is a major challenge for the grid-connected power electronic converter circuits to get synchronized with the utility grid when the grid voltage is highly unbalanced and polluted with harmonics. In this situation designing a phase lock loop (PLL) is a challenge to estimate the phase, frequency, and amplitude of the utility voltage. Phase lock loop (PLLs) is a closed-loop synchronization structure where the error signal is generated from the difference of estimated phases and the reference phase values. This paper presents a filtering technique based on adaptive filter called second-order generalized integrator (SOGI). Under transient fault condition when the three-phase grid signal is highly polluted with harmonics and sudden frequency change or voltage dip occurs, it is a critical task to extract the fundamental component of the grid signal. Here for the extraction of the fundamental component from the polluted signal, two adaptive filters using SOGI can be implemented on the stationary αβ reference frame. To analyze the disturbance rejection capability of the SOGI filter different distortions have been introduced in the three-phase grid signal and the robustness is estimated on the basis of its transient response and harmonic analysis. The simulation results will validate the excellent performance of the filter.

This paper focuses on the stability study of power system integrated with wind energy conversion system (WECS) under various operating conditions. Being a stochastic variation in the input wind speed, WECS affects the stability of the power system. Hence, a hybrid proportional-integral-derivative plus fuzzy logic controller (hPID plus FLC)-based power system stabilizer (PSS) is adopted for improving the stability in single machine infinite bus (SMIB) power system under different operating conditions. The parameters of the proposed hPID plus FLC are optimized using dolphin echolocation optimization (DEO) technique. Several investigations are carried out for assessing the superiority of the proposed technique. It is evident from the graphical as well as the quantitative comparative results using integral of time multiplied absolute error (ITAE), maximum overshoot and the settling time that the proposed DEO tuned hPID plus FLC gives much better performance over GWO and PSO tuned hPID plus FLC controllers.

Hybrid energy system is generally a combination of renewable energy sources like photovoltaic, wind energy and micro-hydro generator. It is integrated with a diesel generator to solve power supply problems in remote places which are far from the grid. Hybrid system is also an economical solution for remote places where grid power availability is limited. The PV and wind energy sources are highly nature dependant and cannot meet the load demand for all times of a day. That is why it requires designing and developing a device to store energy and to meet the peak load demand. In this paper, micro-hydro pumped storage/generator plant has been considered to store the excess energy and also to generate whenever required. A diesel generator has been integrated with hybrid sources to meet the peak load demand. The saliency of this paper is to develop an operational methodology for the nonconventional sources to meet the load demand and to minimize the cost of power generation. A computer programme has been developed to achieve the minimum operational cost of diesel generator.

This paper articulates automatic generation control (AGC) of a three-area multi-unit power system by proposing a fuzzy-aided PID controller incorporated with filter (FPIDF). An inexhaustible photovoltaic (PV) source is injected in the first area of the recommended conventional power system. The challenges arise due to the PV source; a sturdy secondary controller is quintessential. An optimal FPIDF controller is designed by applying a new algorithm named as selfish herd optimization algorithm. To demonstrate the validity of the proposed controller, the transient response evaluated by FPIDF is compared with the PID controller. Further, the robustness is examined by forcing a random step load in area-1.